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Genome Biol. 2018 Oct 25;19(1):173. doi: 10.1186/s13059-018-1546-6.

PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants.

Author information

1
Department of Genetics and Pharmacogenomics, MRL, Boston, MA, USA.
2
Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
3
The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
4
Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. ssunyaev@rics.bwh.harvard.edu.
5
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. ssunyaev@rics.bwh.harvard.edu.
6
The Broad Institute of MIT and Harvard, Cambridge, MA, USA. ssunyaev@rics.bwh.harvard.edu.

Abstract

Functional characterization of the noncoding genome is essential for biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring), which predicts the functional impact of noncoding variants by integrating epigenetic annotations in a phenotype-dependent manner. PINES enables analyses to be customized towards genomic annotations from cell types of the highest relevance given the phenotype of interest. We illustrate that PINES identifies functional noncoding variation more accurately than methods that do not use phenotype-weighted knowledge, while at the same time being flexible and easy to use via a dedicated web portal.

KEYWORDS:

Cell type specificity; Computational functionality prediction; Epigenetic annotations; Epigenetic regulation; Functional scoring; Noncoding variant; Phenotype-relevant scoring; Variant prioritization

PMID:
30359302
PMCID:
PMC6203199
DOI:
10.1186/s13059-018-1546-6
[Indexed for MEDLINE]
Free PMC Article

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